Microservices

This post dives deep into a crucial pattern for event-driven microservices: how to build and maintain a service’s authoritative domain aggregates by consuming and processing events from Apache Kafka, ensuring autonomous state management and reducing inter-service coupling.

This deep dive explores how to maintain data integrity and prevent lost updates in event-sourced Spring Boot microservices by implementing robust Optimistic Concurrency Control, a crucial pattern for building resilient and consistent distributed systems.

Protect your Spring Boot microservices from abuse, resource exhaustion, and cascading failures by implementing robust, distributed API rate limiting using Redis and the Token Bucket algorithm.

This post deep dives into advanced caching techniques, from local in-memory solutions to distributed caches with Redis, and tackles crucial invalidation strategies using Apache Kafka to dramatically improve Spring Boot microservice performance and resilience.

Discover how to fortify your Spring Boot microservices against cascading failures and unreliable dependencies by implementing robust resilience patterns like Circuit Breaker, Retry, and Rate Limiter using Resilience4j.